Techniques for Automatically Correcting Groups of Images

ABSTRACT

Disclosed are various embodiments for a computing device that receives input, from a user, selecting an area of a displayed image, where the area includes the blemish sought to be corrected by the user. The computing device identifies a spot pattern of the blemish to be corrected within the area and a context pattern of the displayed image. The computing device corrects the spot pattern of the blemish within the displayed image. The computing device identifies related images that have context patterns similar to the context pattern of the displayed image. The computing device automatically corrects similar instances of the spot pattern appearing within the related images.

BACKGROUND

Consumer image editing is on the rise with the advent of smartphones andtablets. People can capture photos or other images quickly and storethem in remote “cloud” computing devices for access across variousdevices. Since consumers often capture photos on the go using theirsmartphones or other types of image capturing devices, the photos arenot as professional looking as consumers would like, especially forphotos to be shared or preserved for the future. Even though capturingmoments “as it happens” is the essence of consumer photography, imageediting is often required in order to produce beautiful photographicmoments.

Consumers are not professional photo editors, and it can be intimidatingfor consumers to use professional image editing tools to correct theblemishes in the images. Moreover, using image editing tools isgenerally manual in nature and can be time-consuming to complete editingfor just one photo. Given the nature of consumer photography, there aretypically a large number of photos taken during an event or time period.During that time, if a blemish is present in one photo, it will often bepresent in many or all of the photos taken at that time. Manuallyediting each of the photos individually to correct the blemish can betedious and time-consuming.

SUMMARY

Various aspects of the present invention relate to automaticallycorrecting a blemish in a collection of images. In one implementation, auser provides input to a computing device selecting an area of adisplayed image that includes the blemish sought to be corrected by theuser. The computing device identifies a spot pattern of the blemish tobe corrected within the area and a context pattern of the displayedimage. The spot pattern identified by the computing device is editableby the user. The context pattern may be a multilevel context patterncomprising a plurality of component context patterns. The computingdevice corrects the spot pattern of the blemish within the displayedimage, and the user confirms the correction.

The computing device identifies a collection of related images from theimage library that have context patterns similar to the context patternof the displayed image. Based at least in part upon on the confirmationof the correction in the displayed image and any edits to the spotpattern by the user, the computing device automatically corrects similarinstances of the spot pattern appearing within the collection of relatedimages. Correcting instances of the spot pattern appearing within therelated images may be performed after the user confirms a preview ofsaid correcting in the collection of related images.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, with emphasis instead being placed uponclearly illustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a drawing of a networked environment according to variousembodiments of the present disclosure.

FIGS. 2-4 are pictorial diagrams of an example user interface renderedby a client in the networked environment of FIG. 1 according to variousembodiments of the present disclosure.

FIG. 5 is a flowchart illustrating one example of functionalityimplemented as portions of image correction service executed in acomputing environment in the networked environment of FIG. 1 accordingto various embodiments of the present disclosure.

FIGS. 6 and 7 are pictorial diagrams of images that may be examined bythe image correction service executed in a computing environment in thenetworked environment of FIG. 1 according to various embodiments of thepresent disclosure.

FIG. 8 is a flowchart illustrating another example of functionalityimplemented as portions of image correction service executed in acomputing environment in the networked environment of FIG. 1 accordingto various embodiments of the present disclosure.

FIG. 9 is a pictorial diagram of images that may be examined by theimage correction service executed in a computing environment in thenetworked environment of FIG. 1 according to various embodiments of thepresent disclosure.

FIG. 10 is a schematic block diagram that provides one exampleillustration of a computing environment employed in the networkedenvironment of FIG. 1 according to various embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Disclosed herein are various embodiments relating to automaticallycorrecting a blemish in a collection of images. A user selects an areaof a displayed image that contains a blemish which the user seeks toremove. The selected area acts as a “hint” from which the blemish in thedisplayed image and similar blemishes in other images can beautomatically corrected. Various user actions such as confirmations ofthe corrections and any changes to the corrections are analyzed in orderto improve future corrections, resulting in fewer interventions andchanges to the corrections by the user. In the following discussion, ageneral description of the system and its components is provided,followed by a discussion of the operation of the same.

As used herein, a “blemish” is a mark or similar flaw that a userperceives to impair the appearance of an image. For example, within aphotographic image of a monument, a user may consider birds that areshown in flight around the monument to be a blemish. However, in otherexamples, birds in flight may be the intended subject of the image and,thus, not considered a blemish. As disclosed herein, a blemish may bedistinguished from the desired image content based at least in part uponinput from a user identifying an area of an image that contains theblemish. Similar blemishes present in other images are determined basedon the similarity to the blemish previously identified by the user, aswell as the similarity of the context in which the blemish appears.Returning to the examples using birds, images where birds in flight neara particular monument are a blemish can be distinguished from otherimages of birds as the intended subject using the context of the image,namely the presence of the particular monument as context. Otherexamples of blemishes may include a red-wine stain on a shirt, litter onthe ground, a tattoo on an arm, etc.

As used herein, a “spot pattern” is the portion of the imagerepresenting a blemish that is sought to be corrected in the image. Auser may initially identify an area of an image that contains a blemish.Within this area, the portion of the image representing the blemish(i.e. the spot pattern) is automatically detected using the techniquesdisclosed herein. In some embodiments, the spot pattern may be adjustedby the user in the event the automatically detected spot pattern doesnot precisely capture the blemish. The techniques for identifyingsimilar blemishes in other images is carried out, at least in part, bycomparing spot patterns of the blemishes. For example, in an image of agroup of people, the face of one of the people may contain a scar thatis considered to be a blemish. The pixels or other portion of the imagerepresenting the scar (i.e. the blemish) make up the spot pattern of theimage. As another example, a landscape captured in an image may belittered with debris such as papers, bottles, cans, etc. The individualpieces of debris captured in the image may each be represented by a spotpattern made up of the corresponding pixels of the image.

As used herein, a “context pattern” is a region of the image that issurrounding, around, or near the blemish and thus provides informationabout the context in which the blemish occurs in the image. For example,in the image of the group of people discussed above, the region of theimage representing the face of the particular person containing the scarmay be the context pattern. In some instances, the context pattern maybe a multilevel context pattern that includes various “component”context patterns. Each of the component context patterns of a givenmultilevel context pattern are different regions from the image thateach contain all or a portion of the spot pattern. For example, in theimage captured of a monument along with birds in flight discussed above,the component context patterns for the image may include a full shot ofthe monument that also includes all of the birds in the image, a croppedsubsection of the image that includes all of the birds along with anyportions of the monument within the subsection, a different croppedsubsection of the image that includes one or more of the birds alongwith some portion of the monument within the subsection, etc.

Disclosed herein are various embodiments relating to automaticallycorrecting a blemish in a collection of images. In an exemplaryembodiment, a user selects an area of a displayed image that contains ablemish which the user seeks to remove. The selected area acts as a hintfrom which a spot pattern representing the blemish can be automaticallydetected. In addition, a context pattern representing a context in whichthe blemish occurs in the image is identified. The user is presentedwith a preview of the image that includes an automatic correctionapplied to the spot pattern. If the user approves the automaticcorrection, the corrected image is stored. Alternatively, the user mayadjust the spot pattern and/or correction applied to the blemish (i.e.spot pattern) in the event the previewed correction is not satisfactory.The spot pattern, context pattern, and any changes made by the user arestored in a correction history.

Thereafter, other images are examined in order to identify similar spotpatterns appearing within similar context patterns. The user ispresented with a preview of the other images that include an automaticcorrection applied to the similar spot patterns identified in the otherimages. For each of the automatic corrections approved by the user, thecorresponding corrected image is stored. For each of the automaticcorrections rejected by the user, the user can reject making anycorrection, or the user can adjust the spot pattern and/or correction ofthe blemish in the event the previewed correction is not satisfactory.The spot patterns and context patterns of the other images, approvalsand rejections of corrections, and any changes made by the user arestored in a correction history. The correction history is used to adaptthe identification of similar blemishes in other images and thecorrections to be applied to the images, thereby allowing the automaticcorrection operations to learn from previous activities based onapprovals, rejections, and changes by the user.

With reference to FIG. 1, shown is an illustrative networked environment100 according to various embodiments. The networked environment 100includes a computing environment 103 and a client device 106, which arein data communication with each other via a network 109. The network 109includes, for example, the Internet, intranets, extranets, wide areanetworks (WANs), local area networks (LANs), wired networks, wirelessnetworks, or other suitable networks, etc., or any combination of two ormore such networks. For example, such networks may comprise satellitenetworks, cable networks, Ethernet networks, and other types ofnetworks. Although the functionality described herein is shown in thecontext of the networked environment 100, other implementations arepossible, such as implementing the functionality in a single computingdevice (e.g. desktop computer or mobile device), as a plug-in orauxiliary feature of another service executed in a computing device,and/or in arrangements of computing devices other than those shown inFIG. 1.

The computing environment 103 may comprise, for example, a servercomputer or any other system providing computing capability.Alternatively, the computing environment 103 may employ a plurality ofcomputing devices that may be arranged, for example, in one or moreserver banks or computer banks or other arrangements. Such computingdevices may be located in a single installation or may be distributedamong many different geographical locations. For example, the computingenvironment 103 may include a plurality of computing devices thattogether may comprise a hosted computing resource, a grid computingresource and/or any other distributed computing arrangement. In somecases, the computing environment 103 may correspond to an elasticcomputing resource where the allotted capacity of processing, network,storage, or other computing-related resources may vary over time.

Various applications and/or other functionality may be executed in thecomputing environment 103 according to various embodiments. Also,various data is stored in a data store 112 that is accessible to thecomputing environment 103. The data store 112 may be representative of aplurality of data stores 112 as can be appreciated. The data stored inthe data store 112, for example, is associated with the operation of thevarious applications and/or functional entities described below.

The components executed on the computing environment 103, for example,include an image correction service 121 and other applications,services, processes, systems, engines, or functionality not discussed indetail herein. The image correction service 121 is executed toautomatically correct a particular blemish present in a group of imagesbased in part upon a user identifying the blemish to be removed in oneimage and potentially other image correction history.

The data stored in the data store 112 includes, for example, imagelibrary 131, correction history 133, user data 135, and potentiallyother data. The image library 131 includes images associated withvarious users of the image correction service 121, metadata associatedwith the images, etc. The images can be stored in various formats suchas joint photographic experts group (JPEG), graphics interchange format(GIF), bitmap, raw, tagged image file format (TIFF), and/or other imageformats as can be appreciated. The metadata for an image may include anidentifier for the image capture device used, capture settings used(e.g. flash, shutter speed, etc.), geographic location (“geolocation”)of the capture, time/date of capture, thumbnail images, previousversions of the image, and/or other possible metadata. In someimplementations, one or more of the images may be references (e.g. URLs)to images stored external to the data store 112.

The correction history 133 includes a history of each user's actionsassociated with the corrections undertaken by the image correctionservice 121. For example, the correction history 133 may includeidentifiers of the images in which a user specified a blemish, thearea(s) of the image(s) specified by the user as containing the blemish,any changes by the user made during the automatic correction operations,acceptance and/or rejection by the user of the automatically correctedimages, a history of the changes made to the images, and/or otherpossible historical data.

The user data 135 includes various data associated with users of theimage correction service 121 and/or who have images stored in the imagelibrary 131. The user data 135 may include user credentials, identifiersof images stored by the user, identifiers of images in which the userappears, preferences, and/or other possible data.

The client 106 is representative of a plurality of client devices thatmay be coupled to the network 109. The client 106 may comprise, forexample, a processor-based system such as a computer system. Such acomputer system may be embodied in the form of a desktop computer, alaptop computer, personal digital assistants, cellular telephones,smartphones, set-top boxes, music players, web pads, tablet computersystems, game consoles, electronic book readers, or other devices withlike capability. The client 106 may include a display 161. The display161 may comprise, for example, one or more devices such as liquidcrystal display (LCD) displays, gas plasma-based flat panel displays,organic light emitting diode (OLED) displays, electrophoretic ink (Eink) displays, LCD projectors, or other types of display devices, etc.

The client 106 may be configured to execute various applications such asa client application 163 and/or other applications. The clientapplication 163 may be executed in a client 106, for example, to accessnetwork content served up by the computing environment 103 and/or otherservers, thereby rendering a user interface 165 on the display 161. Tothis end, the client application 106 may comprise, for example, abrowser, a dedicated application, etc., and the user interface 165 maycomprise a network content page, an application screen, etc. The client106 may be configured to execute applications beyond the clientapplication 163 such as, for example, email applications, socialnetworking applications, word processors, spreadsheets, and/or otherapplications.

Next, a general description of the operation of the various componentsof the networked environment 100 is provided. To begin, a user operatingthe client 106 employs the client application 163 to establish acommunication session with the image correction service 121. Thecommunication session may be carried out using various protocols suchas, for example, hypertext transfer protocol (HTTP), simple objectaccess protocol (SOAP), representational state transfer (REST), userdatagram protocol (UDP), transmission control protocol (TCP), and/orother protocols for communicating data over the network 109. In someimplementations, the user is authenticated to the image correctionservice 121 using one or more user credentials.

Thereafter, the user selects, from the user interface 165, an imagehaving a blemish to be corrected, such as shown in FIG. 2. In someembodiments, the selected image may be chosen from the preexistingimages from the image library 131 that are associated with the user,uploaded from the client 106 through the user interface 165, and/orobtained from other possible sources. As illustrated in FIG. 3, once theimage is selected, the user then selects an area 303 of the image thatcontains the blemish that is sought to be corrected, such as an area ofa face that contains a scar. In some embodiments, the area selected bythe user may be all or a significant portion of the image.

The image correction service 121 determines the spot pattern for theblemish in the area 303 using a noise detection algorithm and/or otherpossible techniques. For example, using a noise detection algorithm inconjunction with an auto-highlighting algorithm, a spot pattern of ablemish can be identified within the user-defined area based ondifferences in the noise characteristics of the blemish from otherregions of the area. The noise characteristics can be identified basedupon the degree of variation in saturation, contrast, brightness, etc.in the user-defined area of the image.

In addition, the image correction service 121 determines the contextpattern for the region of the image in which the blemish appears. Forexample, in FIG. 3, the spot pattern is the scar and the context patternis the face in which the scar appears. The context pattern may beidentified using face detection algorithms, edge detection algorithms,and/or similar operations as can be appreciated. For example, using anedge detection algorithm, the context pattern may be identified based onthe arrangement of “edges” in a region of the image where the spotpattern is located. An edge can be identified based upon theorganization of points in the image at which image brightness has adiscontinuity. Although this particular example in FIG. 3 illustratesdetection of a scar on the face of a person, detection of other types ofblemishes in other types of contexts are also possible, examples ofwhich are discussed later in the present disclosure.

Subsequently, the image correction service 121 may preview the correctedimage to the user in order to confirm that the correction operations areperformed as expected. In some implementations, the preview may allowthe user to edit the spot pattern of the blemish to be corrected, editthe corrections made to the spot pattern, and/or other possibilities.The spot pattern correction may be carried out using various algorithms,such as Content-Aware Fill technology in Adobe® Photoshop® availablefrom Adobe Systems, Inc. of San Jose, Calif., which will determine areplacement for a selected area of an image based on the surroundingarea of the image.

Once the user confirms the corrections made to the selected image, imagecorrection service 121 may store the spot pattern and context pattern ofthe image, as well as any changes made by the user, to the correctionhistory 133. The corrected image is stored to the image library 131, tothe client 106, and/or to other possible locations.

Subsequently, the image correction service 121 prompts the user todetermine if other images should be examined in order to identify andcorrect similar spot patterns (i.e. similar blemishes) appearing withinthe other images that have similar context patterns. If the user choosesto examine the other images, the image correction service 121 may beginexamining the other images associated with the user that are specifiedin the image library 131. For example, other images that the user mayhave stored in the image library 131 and elsewhere will be examined inorder to find a similar context pattern (e.g. a similar face) having asimilar spot pattern (e.g. facial scar).

In some embodiments, the examination of other images may be carried outin an order based on the temporal context, geographic location context,and/or other characteristics of the original selected image. Forexample, if the image the user originally selected to correct wascaptured on Jan. 1, 2014, the image correction service 121 may beginexamining other images of a proximate capture time and date to theoriginal image based on the presumption that images from a similar timeare more likely to exhibit a similar blemish. Likewise, the imagecorrection service 121 may begin examining other images of a proximategeographic location to the original image based on the presumption thatimages from a similar location are more likely to exhibit a similarblemish.

As shown in FIG. 4, the user interface 165 for the image correctionservice 121 then presents other images 401 a-c found to have a spotpattern 405 within a context pattern similar to the corresponding spotpattern and context pattern of the originally selected image. Similarityof the patterns can be determined based upon pattern shapes such as canbe performed by face matching algorithms and/or other object matchingtechniques as can be appreciated. For these detected images 401 a-c, theimage correction service 121 may preview corrections to those images inorder to confirm that the correction operations are performed asdesired. The corrections made by the image correction service 121 toeach of these detected images 401 a-c may be tailored based on thecorrection history 133, which captured changes made by the user to thepast automatic corrections of other images. These past changes made bythe user may include, for example, changes to the area of the spotpattern, changes to the fill applied to the spot pattern, etc. Inaddition, the preview may allow the user to further adjustcharacteristics of the correction applied to each of these images 401a-c detected by the image correction service 121.

Once the user confirms the corrections made to the detected images 401a-c, the image correction service 121 may store the similar spotpatterns and similar context patterns of the these images 401 a-c, aswell as any changes made by the user, to the correction history 133. Thecorrected images may then be stored to the image library 131, to theclient 106, and/or to other possible locations.

In some embodiments, images subsequently added by the user to the imagelibrary 131 may be examined in order to detect similar spot patterns andcontext patterns to images for which a correction has been previouslyapplied. In the event similar spot patterns and context patterns aredetected, the image correction service 121 may prompt the user todetermine if the newly added image should be corrected as well. Forexample, the image correction service 121 may have previously correctedvarious images then existing in the image library 131 of a particularperson having a scar on her face. If, at a later time, the usertransfers an additional image of the person to the image library 131,the image correction service 121 can recognize their face in the imageand examine it to determine if the scar is present in the new image. Ifit is present, the image correction service 121 may prompt the user tocorrect this new image as had been previously done for other images ofthe person. Similarly, a new image may be examined at the time the imageis captured, for example, by a smart phone camera using a local orremotely-accessed image correction service.

Throughout FIGS. 2-4, an illustrative example of correcting a blemish ona face has been used to describe the operation of the image correctionservice 121. However, as can be appreciated, correcting of other objectsor blemishes that may be present in different areas of an image (i.e.other than a face) are also possible.

Referring next to FIG. 5, shown is a flowchart that provides one exampleof the operation of a portion of the image correction service 121according to various embodiments. It is understood that the flowchart ofFIG. 5 provides merely an example of the many different types offunctional arrangements that may be employed to implement the operationof the portion of the image correction service 121 as described herein.As an alternative, the flowchart of FIG. 5 may be viewed as depicting anexample of elements of a method implemented in the computing environment103 according to one or more embodiments. The steps depicted in theflowchart of FIG. 5 may be implemented once a user has beenauthenticated to the image correction service 121 and has selected animage to which a correction should be applied to a blemish.

Beginning with block 503, the user then selects one or more areas of theimage containing a blemish that is sought to be corrected. For example,in FIG. 6, the areas 603 a-b were selected by the user that captureportions of the image 600 of the Taj Mahal that include birds which theuser wishes to remove from the image (i.e. blemishes). Next, in block506 of FIG. 5, the image correction service 121 determines the contextpattern for the region of the image 600 in which the blemish appears.The context pattern may be identified using edge detection algorithms,face detection algorithms, and/or similar operations as can beappreciated. In an embodiment, the context pattern may be automaticallyidentified as an area surrounding the blemish within a predeterminedradius or bounding area of a fixed size. The size of the context patternmay be based on the size of the user-selected blemish area. For example,the context area may be a circle, oval, or square having double, triple,etc. the size of the user-selected blemish area and may be centered onthe center of the user-selected blemish area.

In some embodiments, the image correction service 121 may determine thata multilevel context pattern should be used for a given image. Thedetermination that a multilevel context pattern is needed can be basedupon an edge detection algorithm determining that a threshold number ofedges are present in the original image (i.e. having too many edges), auser preference defined in the user data 135, and/or other possiblecriteria as can be appreciated. By identifying various component contextpatterns for the image that include one or more of the areas selected bythe user, the image correction service 121 can improve the likelihood offinding other images with a similar context pattern to at least one ofthe component context patterns.

For example, returning to FIG. 6, if the context pattern used is aslarge as the captured image of the Taj Mahal itself, then it is likelythat only other images of the Taj Mahal from the same perspective wouldbe considered to have a similar context pattern, thus resulting inautomatic correction of few other images. Therefore, the imagecorrection service 121 may generate a multilevel context pattern fromthe image 600 such as shown in FIG. 7. In FIG. 7, the exemplarycomponent context patterns 707 a-e of the multilevel context pattern areshown representing various views derived from the original image 600that each capture one or more of the areas 603 a-b identified by theuser. The number of component context patterns created from an image candepend upon the number of areas identified by the user, the number ofedges present in each of the component context patterns, a userpreference defined in the user data 135, and/or other possible criteriaas can be appreciated.

Each of the views of the component context patterns 707 a-e may be adifferent cropped region from the image 600 that capture some or all ofthe areas 603 a-b, while also capturing different portions of thecontext of the image 600 in which these captured areas appear.Continuing the example, the component context pattern 707 a is a fullshot of the monument in the image 600 that also includes all of thebirds (i.e. the blemishes) in the image, the component context pattern707 d is a cropped and enlarged subsection of the image 600 thatincludes all of the birds along with the dome portion of the monument,the component context pattern 707 e is a different cropped and enlargedsubsection of the image that includes only one of the birds along withanother part of the dome of the monument, etc. By identifying variouscomponent context patterns 707 a-e for the image 600 that include one ormore of the areas 603 a-b selected by the user, the image correctionservice 121 can improve the likelihood of finding other images with asimilar context pattern to at least one of the various component contextpatterns 707 a-e, as opposed to a single context pattern.

Then, in block 507 of FIG. 5, image correction service 121 determinesthe spot pattern for the blemish in the area selected by the user, suchas the area 603 a-b, using a noise detection algorithm or other possibletechniques.

Subsequently, in block 509, the image correction service 121 may previewthe corrected image to the user in order to allow the user to confirmthat the correction operations are performed as expected.

Next, in block 512, the image correction service 121 determines whetherthe user accepts or confirms the corrections made to the image.

If the user seeks to make corrections, in block 515, the imagecorrection service 121 may allow the user to edit the spot pattern ofthe blemish to be corrected, edit the corrections made to the spotpattern, and/or other possibilities. Thereafter, execution of the imagecorrection service 121 returns to block 509. Alternatively, if the userconfirms the corrections made to the image, in block 518, the imagecorrection service 121 may store the spot pattern and context pattern ofthe image, including a multilevel context pattern, as well as anychanges made by the user, to the correction history 133. Then, in block521, the image correction service 121 may implement the correctionsconfirmed by the user and store the corrected image to the image library131, to the client 106, and/or to other possible locations.

Subsequently, in block 524, the image correction service 121 prompts theuser to determine if other images should be examined in order toidentify and correct similar spot patterns (i.e. the blemish) appearingwithin the other images that have similar context patterns. If the userchooses not to search for any additional images to be corrected, thenthis portion of the execution of the image processing service may end asshown.

Alternatively, if the user chooses to examine the other images, then theflow chart continues in the flow chart of FIG. 8. Beginning with block803 in FIG. 8, the image correction service 121 may begin examining theother images associated with the user that are specified in the imagelibrary 131 or elsewhere. This may include, for example, other imagesthat the user may have stored in the image library 131, images stored onthe client 106, other computing devices accessible via the network 109,and/or other locations as can be appreciated. The available images willbe examined in order to find a similar spot pattern (e.g. birds inflight) in a region of the image having a similar context pattern (e.g.resembling one or more of the component context patterns).

In some embodiments, the examination of other images may be carried outbased on the temporal context, geographic location context, and/or othercharacteristics reported in the metadata of the original selected image.For example, if the image the user originally selected to correct wascaptured on Jan. 1, 2014, the image correction service 121 may beginexamining other images of a proximate capture time and date to theoriginal image based on the presumption that images from a similar timeare more likely to exhibit a similar blemish. Likewise, the imagecorrection service 121 may begin examining other images of a proximategeographic location to the original image based on the presumption thatimages from a similar location are more likely to exhibit a similarblemish. Thereafter, the search of images may be expanded to otherlocations, times, dates, etc., as may be configured by the user.

Then, in block 809, the user interface 165 for the image correctionservice 121 then presents other images found to have a spot pattern in aregion having a context pattern similar to the spot pattern and contextpattern of the originally selected image 600. For these detected images,the image correction service 121 may preview corrections to them inorder to confirm that the correction operations are performed asexpected. The corrections made by the image correction service 121 toeach of these detected images may be tailored based on the correctionhistory 133, which captured any changes made by the user to the pastautomatic corrections of the original image 600 and potentially otherimages. These past changes made by the user may include, for example,changes to the area of the spot pattern, changes to the fill applied tothe spot pattern, etc.

Next, in block 812, the image correction service 121 determines whetherthe user accepts or confirms the corrections made to the detectedimages. If the user seeks to make correction, in block 815, the imagecorrection service 121 may allow the user to edit the spot pattern ofthe blemish to be corrected, edit the corrections made to the spotpattern, remove an image from the set of detected images, and/or otherpossibilities. Thereafter, execution of the image correction service 121returns to block 809.

Alternatively, if the user confirms the corrections made to the image,in block 818, the image correction service 121 may store the similarspot patterns and similar context patterns of the detected image, anychanges made by the user, metadata associated with the images, and/orother possible data to the correction history 133. Then, in block 821,the image correction service 121 may implement the corrections confirmedby the user and store the corrected images to the image library 131, tothe client 106, and/or to other possible locations. Next, in block 824,the image correction service 121 determines whether additional imagesshould be examined for correction. These additional images may includeimages that will be added to the image library 131 in the future,existing images in the image library 131 that may now be identifiedbased recent corrections performed by the image correction service 121,and/or other possible images. Additional images may be detected andanalyzed for blemishes as images are created on or received at a newdevice, such as a laptop, camera, or smartphone, or when images areadded to a collection of images. For example, a social networkingwebsite may provide an image analysis function that is applied to allimages that a user uploads to the social networking website so that auser does not have to repeatedly delete the same mole from every selfimage.

In FIG. 9, shown are detected images 901 a-b that were among otherimages similarly matching one or more of the component context patterns707 a-e. Using metadata for the detected images 901 a-b and the originalimage 600, the image correction service 121 can determine that, despitethe similarity with one or more of the component context patterns, theimage capture locations are of different landmarks. However, ifcorrections were applied to the images 901 a-b and confirmed by theuser, the image correction service 121 may infer that the spot andcontext patterns present in the images 901 a-b should be added to theexisting collection of spot and context pattern sets used to examineother images within the image library 131 or elsewhere. These otherimages can include images that would not otherwise be a similarity matchto one or more of the component context patterns 707 a-e, but that are asimilarity match to the context patterns present in 901 a-b.

Continuing, if the user chooses to examine other images for automaticcorrection, then execution of the image correction service may return toblock 803. Alternatively, if the user chooses not to examine additionalimages, this portion of the execution of the image correction service121 ends as shown.

With reference to FIG. 10, shown is a schematic block diagram of thecomputing environment 103 according to an embodiment of the presentdisclosure. The computing environment 103 includes one or more computingdevices 1000. Each computing device 1000 includes at least one processorcircuit, for example, having a processor 1003, a memory 1006, and anetwork interface 1007, all of which are coupled to a local interface1009. To this end, each computing device 1000 may comprise, for example,at least one server computer or like device. The local interface 1009may comprise, for example, a data bus with an accompanyingaddress/control bus or other bus structure as can be appreciated.

Stored in the memory 1006 are both data and several components that areexecutable by the processor 1003. In particular, stored in the memory1006 and executable by the processor 1003 is the image correctionservice 121, and potentially other applications. Also stored in thememory 1006 may be a data store 112 and other data. In addition, anoperating system may be stored in the memory 1006 and executable by theprocessor 1003.

It is understood that there may be other applications that are stored inthe memory 1006 and are executable by the processor 1003 as can beappreciated. Where any component discussed herein is implemented in theform of software, any one of a number of programming languages may beemployed such as, for example, C, C++, C#, Objective C, Java®,JavaScript , Perl, PHP, Visual Basic®, Python®, Ruby, Flash®, or otherprogramming languages.

A number of software components are stored in the memory 1006 and areexecutable by the processor 1003. In this respect, the term “executable”means a program file that is in a form that can ultimately be run by theprocessor 1003. Examples of executable programs may be, for example, acompiled program that can be translated into machine code in a formatthat can be loaded into a random access portion of the memory 1006 andrun by the processor 1003, source code that may be expressed in properformat such as object code that is capable of being loaded into a randomaccess portion of the memory 1006 and executed by the processor 1003, orsource code that may be interpreted by another executable program togenerate instructions in a random access portion of the memory 1006 tobe executed by the processor 1003, etc. An executable program may bestored in any portion or component of the memory 1006 including, forexample, random access memory (RAM), read-only memory (ROM), hard drive,solid-state drive, USB flash drive, memory card, optical disc such ascompact disc (CD) or digital versatile disc (DVD), floppy disk, magnetictape, or other memory components.

The memory 1006 is defined herein as including both volatile andnonvolatile memory and data storage components. Volatile components arethose that do not retain data values upon loss of power. Nonvolatilecomponents are those that retain data upon a loss of power. Thus, thememory 1006 may comprise, for example, random access memory (RAM),read-only memory (ROM), hard disk drives, solid-state drives, USB flashdrives, memory cards accessed via a memory card reader, floppy disksaccessed via an associated floppy disk drive, optical discs accessed viaan optical disc drive, magnetic tapes accessed via an appropriate tapedrive, and/or other memory components, or a combination of any two ormore of these memory components. In addition, the RAM may comprise, forexample, static random access memory (SRAM), dynamic random accessmemory (DRAM), or magnetic random access memory (MRAM) and other suchdevices. The ROM may comprise, for example, a programmable read-onlymemory (PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or otherlike memory device.

Also, the processor 1003 may represent multiple processors 1003 and/ormultiple processor cores and the memory 1006 may represent multiplememories 1006 that operate in parallel processing circuits,respectively. In such a case, the local interface 1009 may be anappropriate network that facilitates communication between any two ofthe multiple processors 1003, between any processor 1003 and any of thememories 1006, or between any two of the memories 1006, etc. The localinterface 1009 may comprise additional systems designed to coordinatethis communication, including, for example, performing load balancing.The processor 1003 may be of electrical or of some other availableconstruction.

Although the image correction service 121, and other various systemsdescribed herein may be embodied in software or code executed by generalpurpose hardware as discussed above, as an alternative the same may alsobe embodied in dedicated hardware or a combination of software/generalpurpose hardware and dedicated hardware. If embodied in dedicatedhardware, each can be implemented as a circuit or state machine thatemploys any one of or a combination of a number of technologies. Thesetechnologies may include, but are not limited to, discrete logiccircuits having logic gates for implementing various logic functionsupon an application of one or more data signals, application specificintegrated circuits (ASICs) having appropriate logic gates,field-programmable gate arrays (FPGAs), or other components, etc. Suchtechnologies are generally well known by those skilled in the art and,consequently, are not described in detail herein.

The flowcharts of FIGS. 5 and 8 show the functionality and operation ofan implementation of portions of the image correction service 121. Ifembodied in software, each block may represent a module, segment, orportion of code that comprises program instructions to implement thespecified logical function(s). The program instructions may be embodiedin the form of source code that comprises human-readable statementswritten in a programming language or machine code that comprisesnumerical instructions recognizable by a suitable execution system suchas a processor 1003 in a computer system or other system. The machinecode may be converted from the source code, etc. If embodied inhardware, each block may represent a circuit or a number ofinterconnected circuits to implement the specified logical function(s).

Although the flowcharts of FIGS. 5 and 8 show a specific order ofexecution, it is understood that the order of execution may differ fromthat which is depicted. For example, the order of execution of two ormore blocks may be scrambled relative to the order shown. Also, two ormore blocks shown in succession in FIGS. 5 and 8 may be executedconcurrently or with partial concurrence. Further, in some embodiments,one or more of the blocks shown in FIGS. 5 and 8 may be skipped oromitted. In addition, any number of counters, state variables, warningsemaphores, or messages might be added to the logical flow describedherein, for purposes of enhanced utility, accounting, performancemeasurement, or providing troubleshooting aids, etc. It is understoodthat all such variations are within the scope of the present disclosure.

Also, any logic or application described herein, including the imagecorrection service 121, that comprises software or code can be embodiedin any non-transitory computer-readable medium for use by or inconnection with an instruction execution system such as, for example, aprocessor 1003 in a computer system or other system. In this sense, thelogic may comprise, for example, statements including instructions anddeclarations that can be fetched from the computer-readable medium andexecuted by the instruction execution system. In the context of thepresent disclosure, a “computer-readable medium” can be any medium thatcan contain, store, or maintain the logic or application describedherein for use by or in connection with the instruction executionsystem.

The computer-readable medium can comprise any one of many physical mediasuch as, for example, magnetic, optical, or semiconductor media. Morespecific examples of a suitable computer-readable medium would include,but are not limited to, magnetic tapes, magnetic floppy diskettes,magnetic hard drives, memory cards, solid-state drives, USB flashdrives, or optical discs. Also, the computer-readable medium may be arandom access memory (RAM) including, for example, static random accessmemory (SRAM) and dynamic random access memory (DRAM), or magneticrandom access memory (MRAM). In addition, the computer-readable mediummay be a read-only memory (ROM), a programmable read-only memory (PROM),an erasable programmable read-only memory (EPROM), an electricallyerasable programmable read-only memory (EEPROM), or other type of memorydevice.

Further, any logic or application described herein, including the imagecorrection service 121, may be implemented and structured in a varietyof ways. For example, one or more applications described may beimplemented as modules or components of a single application. Further,one or more applications described herein may be executed in shared orseparate computing devices or a combination thereof. For example, aplurality of the applications described herein may execute in the samecomputing device 1000, or in multiple computing devices in the samecomputing environment 103. Additionally, it is understood that termssuch as “application,” “service,” “system,” “engine,” “module,” and soon may be interchangeable and are not intended to be limiting.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z).Thus, such disjunctive language is not generally intended to, and shouldnot, imply that certain embodiments require at least one of X, at leastone of Y, or at least one of Z to each be present.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

Therefore, the following is claimed:
 1. A method comprising: a computingdevice: receiving input selecting an area of an image that includes ablemish to be corrected; identifying a context pattern comprising aregion of the image representing a context in which the blemish occursin the image; identifying a spot pattern of the blemish within the area;correcting the spot pattern of the blemish within the image; identifyingrelated images having context patterns similar to the context pattern ofthe image; and correcting similar instances of the spot patternappearing within the related images.
 2. The method of claim 1, wherein auser confirms said correcting within the image.
 3. The method of claim1, further comprising a user interface through which the computingdevice receives the input selecting the area of the image that includesthe blemish.
 4. The method of claim 1, further comprising: storing atleast the spot pattern and the context pattern of the image in acorrection history; and automatically examining newly added images forblemish correction based at least in part upon the correction history.5. The method of claim 1, wherein the related images are specified in animage library.
 6. The method of claim 1, wherein the related images areexamined in an order based upon a date of capture of the respectiveimages, beginning with ones of the related images captured nearest intime to the image.
 7. The method of claim 1, wherein the context patternof the image is a multilevel context pattern and the context patterns ofthe related images are similar to at least one component context patternof the multilevel context pattern.
 8. The method of claim 1, furthercomprising: identifying a collection of other images having contextpatterns similar to at least one of the context patterns of the relatedimages; and based at least in part upon on a confirmation by the user,correcting similar instances of the spot pattern appearing within thecollection of other images.
 9. The method of claim 1, wherein the spotpattern is identified using an noise detection algorithm.
 10. The methodof claim 1, wherein said correcting instances of the spot patternappearing within the collection of related images is performed after theuser confirms a preview of said correcting in the collection of relatedimages.
 11. The method of claim 1, wherein the spot pattern is correctedusing content-aware fill technology.
 12. A non-transitorycomputer-readable medium embodying a program executable in at least onecomputing device, comprising code that: receives input, from a user,selecting an area of a displayed image, wherein the area includes theblemish sought to be corrected by the user; identifies a spot pattern ofthe blemish to be corrected within the area and a context pattern of thedisplayed image, wherein said identified spot pattern is editable by theuser; corrects the spot pattern of the blemish within the displayedimage, wherein the user confirms said correcting within the displayedimage; identifies a collection of related images from the image libraryhaving context patterns similar to the context pattern of the displayedimage; and based at least in part upon on said confirmation and any saidedit by the user, automatically corrects similar instances of the spotpattern appearing within the collection of related images.
 13. Thenon-transitory computer-readable medium of claim 12, wherein the contextpattern is detected using an edge detection algorithm.
 14. Thenon-transitory computer-readable medium of claim 12, wherein the programfurther comprises code that: identifies a collection of other imagesfrom the image library having context patterns similar to at least oneof the context patterns of the related images; and based at least inpart upon on said confirmation and any said edit by the user, correctssimilar instances of the spot pattern appearing within the collection ofother images.
 15. The non-transitory computer-readable medium of claim12, wherein the images in the image library are examined for relatedimages in an order based upon a capture location of the respectiveimages, beginning with ones of the images captured nearest in locationto the displayed image.
 16. The non-transitory computer-readable mediumof claim 12, wherein the program further comprises code that: stores atleast the spot pattern and the context pattern of the displayed image ina correction history; and automatically examines images newly added tothe image library for blemish correction based at least in part upon thecorrection history.
 17. A system, comprising: at least one computingdevice; and an image correction service executed in the at least onecomputing device, the image correction service comprising logic that:receives input, from a user, selecting an area of a displayed image,wherein the area includes the blemish sought to be corrected by theuser; identifies a spot pattern of the blemish to be corrected withinthe area and a context pattern of the displayed image, wherein saididentified spot pattern is editable by the user; corrects the spotpattern of the blemish within the displayed image, wherein the userconfirms said correcting within the displayed image; identifies acollection of related images from the image library having contextpatterns similar to the context pattern of the displayed image; andbased at least in part upon on said confirmation and any said edit bythe user, automatically corrects similar instances of the spot patternappearing within the collection of related images.
 18. The system ofclaim 17, wherein the image correction service further comprises logicthat: identifies a collection of other images from the image libraryhaving context patterns similar to at least one of the context patternsof the related images; and based at least in part upon on saidconfirmation and any said edit by the user, corrects similar instancesof the spot pattern appearing within the collection of other images. 19.The system of claim 17, wherein the image correction service furthercomprises logic that: stores at least the spot pattern and the contextpattern of the displayed image in a correction history; andautomatically examines images newly added to the image library forblemish correction based at least in part upon the correction history.20. The system of claim 17, wherein the context pattern of the displayedimage is a multilevel context pattern and the context patterns of therelated images are similar to at least one component context pattern ofthe multilevel context pattern.